An AI-Driven Decision Support Framework for Ergonomic Optimization in Fashion Manufacturing: Integrating Predictive Analytics and MCDM Techniques

Yunfan Zhang; Eakachat Joneurairatana; Jirawat Vongphantuset

doi:10.31181/dmame7120241449

Authors

Yunfan Zhang PHD CandidatesFaculty of Decorative Arts，Silpakorn University, Bangkok, Thailand, 10170. https://orcid.org/0009-0007-2036-4273
Eakachat Joneurairatana Professor, Faculty of Decorative Arts, Silpakorn University, Bangkok, Thailand, 10170. https://orcid.org/0009-0001-4813-7327
Jirawat Vongphantuset Assistant Professor, Faculty of Decorative Arts, Silpakorn University, Bangkok, Thailand, 10170. https://orcid.org/0009-0005-1809-0061

DOI:

https://doi.org/10.31181/dmame7120241449

Keywords:

Artificial Intelligence, Predictive Analytics, Sustainable Fashion, Supply Chain Optimization, Trend Forecasting.

Abstract

The integration of Artificial Intelligence (AI) with predictive analytics is catalysing digital transformation within the fashion industry, reshaping operational procedures and influencing decision-making practices. This study introduces a decision-making framework that leverages predictive modelling techniques in conjunction with Multi-Criteria Decision-Making (MCDM) methods to enhance ergonomics in fashion manufacturing. The framework recognises the pivotal role of AI in facilitating automated design processes and inventory forecasting, alongside optimising supply chain operations to promote sustainable ergonomic practices. Specifically, the framework employs Autoencoder Recurrent Neural Network (RNN) models—advanced deep learning methods—to deliver improved accuracy in forecasting customer demand and identifying consumer preferences. AI-powered generative design contributes to reduced material waste and enhanced production efficiency, aligning with the goals of operational excellence and ergonomic compliance. Moreover, intelligent logistics systems and Internet of Things (IoT)-driven analytics within supply chain management support cost reduction and risk mitigation efforts. To systematically assess and prioritise ergonomic considerations, the Analytic Hierarchy Process (AHP) technique is incorporated into the framework. This integration facilitates structured evaluations of alternatives, enabling transparent and data-informed decisions that strike a balance between worker comfort, sustainability, and productivity. Additionally, AI applications in fashion retail enhance the consumer experience by enabling virtual product trials, delivering personalised recommendations, and providing interactive digital support. The adoption of AI in fashion retail is largely attributed to its capabilities in simulating product testing and offering tailored customer services, including digital assistance. The proposed framework thus supports sustainable manufacturing practices and worker well-being while fostering robust, data-centric managerial decision-making. Ultimately, the integration of MCDM with predictive analytics and AI forms the foundation for achieving responsible operations and human-centred production design, which are critical for the fashion sector's long-term viability.

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